What is variance useful for in data analysis?

Variance is a statistical measurement that reflects how much the values in a dataset differ from the mean (average). By quantifying this dispersion, variance serves as a crucial tool for identifying outliers. An outlier is a value that significantly deviates from other observations, and this deviation can be quantitatively assessed through variance.

When variance is high, it indicates that the data points are spread out over a wider range of values, which may suggest the presence of outliers. Conversely, a low variance means that the data points tend to be close to the mean, suggesting a more uniform distribution. Analyzing variance helps researchers and analysts discern whether specific values are atypical compared to the rest of the dataset. Thus, its application in identifying outliers is essential in data analysis, enabling better understanding and decision-making regarding the dataset.

The other options, while relevant to different aspects of data analysis, do not directly relate to the concept of variance as it pertains to the assessment of outliers in the data. Identifying average values, determining frequency, and creating histograms focus on different statistical measures and processes that do not inherently utilize variance in the same way.